online monitoring and reconstruction
DESCRIPTION
Online Monitoring and Reconstruction. Linda R. Coney 4 June, 2009. Outline. Introduction Data Structure Unpacking DATE data Online Monitoring Online Reconstruction Conclusions. MICE Online. So far: DAQ front end Trigger Event Building Controls and Monitoring - PowerPoint PPT PresentationTRANSCRIPT
Linda R. Coney – 24th April 2009
Online Monitoring and Reconstruction
Linda R. Coney
4 June, 2009
Linda R. Coney – 4 June 2009
Outline
Introduction Data Structure Unpacking DATE data Online Monitoring Online Reconstruction Conclusions
Linda R. Coney – 4 June 2009
MICE Online
So far: DAQ front end Trigger Event Building Controls and Monitoring
Given that we are successfully running the experiment and creating data How do we know the equipment is working well? How do we check the data quality?
Two levels of real-time data quality checks Online Monitoring
Look at raw data for each board in the DAQ No translation into physical quantities
Online Reconstruction Initial look at analysis variables
Next: see Henry’s talk about the Data Flow…
Linda R. Coney – 4 June 2009
DAQ Terminology
LDC – Local Data Collector
GDC – Global Data Collector
Equipment – module in DAQ crate
DATE – The DAQ Software
Linda R. Coney – 4 June 2009
Data Format
DAQ Events: SuperEvent contains SubEvents
come from single crate (ie. come from LDC) Header for Super/Sub events is the same Event Fragment is data from single board in crate (equipment)
Fragments have different information for different board types Two types of Events
CALIBRATION– Always 1 particle event
PHYSICS– Can have multiple particle events– Should have 2 crates– Data volume dominated by fADCs
Particle event info is board specific
Linda R. Coney – 4 June 2009
Raw Data Format
…
DAQ Event N+1 Payload…
DAQ Event N+1 GDC Header
DAQ Event NPayload…
DAQ Event NGDC Header
…
Run File
…
LDC J+1 Payload…
LDC J+1 Header
LDC J Payload…
LDC J Header
…
(Super-) Event
…
Particle Event M+1Data: Board Manufacturer Format
Particle Event M Data: Board Manufacturer Format
…
Event Fragment
…
Equipment K+1 Payload
Equipment K+1 Header
Equipment K Payload
Equipment K Header
…
(Sub-) Event
Linda R. Coney – 4 June 2009
DATE Event Header Format
Event Header
Event Size
Sync. Word
Header Size
Header Version
EventType
RunNb
Event Id[0]
Event Id[1]
TriggerPattern[0]
TriggerPattern[1]
DetectorPattern[0]
DetectorPattern[1]
Attribute[0]
Attribute[1]
Attribute[2]
LDC Id
GDC Id
TimeStamp[0]
TimeStamp[1]
This structure comes from DATE
Linda R. Coney – 4 June 2009
DATE Equipment Header Format
Equipment Data Size
Equipment Type
Equipment User Id
Equipment Attribute[0]
Equipment Attribute[1]
Equipment Attribute[2]
Equipment Word Size
Equipment Header
Conventional Table of Equipment Type:
Random Generator0
Scalar V830111
VLSB104
fADC V1724120
Trailer110
TDC V1290102
Trigger Receiver101
V2718100
EquipmentType
Linda R. Coney – 4 June 2009
V. Verguilov
Particle Data Format Example - CAEN V1290 TDC
31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0
0 1 0 0 0 Event Count (starting at 0) GEO Address
0 0 0 0 0 T Channel Nb Time Data
.
.
.
.
.
.
.
.
.
.
.
.
……Variable Number of hits…
…
0 0 0 0 0 T Channel Nb Time Data
1 0 0 0 0 Status Word Count GEO Address
TDC V1290
…
Particle Event M Data: Board Manufacturer Format
Particle Event M+1Data: Board Manufacturer Format
…
Data Type
T = 1 for Trailing Edge Measurement Data
Linda R. Coney – 4 June 2009
Data Unpacking Classes
Linda R. Coney – 4 June 2009
Data Unpacking Classes
MDdataContainer - base class for all
MDEvent – handles sub and super events
MDeventFragment - container for the particle events, data from single board
MDpartEventXXX - classes manipulating the data (at event level) from each equipment using corresponding MDdataWordXXX class
MDpartEventV1724: GetPattern, GetChannelMask, GetTriggerTimeTag, GetSampleData (fADC) MDpartEventV1290: GetHitMeasurement, GetHitType, GetHitChannel, GetNHits (TDC)
MDequipMap - Class using a hash to determine which object (MDpartEventXXX) can decode specific event, based on the Equipment Id of the event
MDdataWord - base class for word-level classes (SetDataWord( void * d))
MDdataWordXXX - classes implementing the data format (at word-level) of each equipment MDdataWordV1724: GetSample MDdataWordV1290: GetMeasurement, GetChannel, GetTDC, GetError, GetWordCount, GetBunchID,
GetEventID
MDdateFile - IO routines for the DATE raw data file
MDargumentHandler – class for manipulating command-line input
Linda R. Coney – 4 June 2009
Unpacking Flow Chart
Linda R. Coney – 4 June 2009
Online Monitoring
Linda R. Coney – 4 June 2009
Online Monitoring
Run unpacker on DATE data Fill plots for each type of board
No geography information No reconstruction Boards have ID# but no information on what channel it is
Fill online monitoring histograms in real time while taking data Use to debug operations Provides data quality check Provide graphical interface to display plots
There are 3 overall types of plots because there are 3 types of board FADCs Scalar TDCs
Linda R. Coney – 4 June 2009
Scalar in DAQ
Scalars count hits inside the DAQ Spill GateScalars count hits inside the DAQ Spill GateP
art.
Tri
gg
erP
art.
Trg
Req
.G
VA
1
GV
A2
GV
A3
CKOVA/B
Clo
ck 1
MH
zT
OF
0
Cumulative, average and Last Spill Available
Linda R. Coney – 4 June 2009
Online Monitoring Histograms
Example of monitoring plots from data run in November08
Preset histograms
TOF position info, Scalars
Linda R. Coney – 4 June 2009
Online Monitoring Actions
DAQ DATE Readout is finished Create framework for decoding data Implement unpacking for TOF, CKOV, KL Test data readout, unpacking, and monitoring with real-time data Include unpacking with G4MICE Create online monitoring plots for TOF, CKOV
Upgrade FADC firmware (7/09) Will decrease size of data
Modify FADC monitoring plots (7/09) Implement unpacking for Tracker (08/09) Create online monitoring plots for KL,Tracker, EMR (9/09, 2010) Implement unpacking for EMR (2009)
Linda R. Coney – 4 June 2009
Online Reconstruction
Linda R. Coney – 4 June 2009
Online Reconstruction
G4MICE uses the unpacker to look at data from DATE It then converts the raw data into information with physical meaning Goal:
Provide a fixed set of histograms to be filled in real time during data taking These histograms will contain quantities that can give information about the
physics happening – first look at analysis quantities Provides another data quality check
Are we taking the data we think we are? Are the detectors & beam behaving as planned?
Provide graphical interface to display plots Not meant to be final results Collaboration chooses list of useful histograms
Linda R. Coney – 4 June 2009
Online Reconstruction Histograms
TOF Reconstructed time-of-flight Distribution in x, y across TOF0, TOF1, TOF2 2D x vs y gives shape of beam
CKOV Light yield
KL EMR Tracker(s)
Muon px, py, pz, pT, p at the 2 tracker reference planes x,x’, y,y’ 1D, 2D plots of position at 2 tracker reference planes Light yield distributions for each station
PID determination Beam emittance, amplitude
Linda R. Coney – 4 June 2009
Online Reconstruction Histograms
What is needed to produce these plots? Online Reconstruction farm G4MICE installed on farm TOF reconstruction CKOV reconstruction Tracker reconstruction KL reconstruction
Unpacking code for each detector Check that G4MICE uses unpacker in a same way that Online Monitoring
uses unpacker
Linda R. Coney – 4 June 2009
Current Status of Reconstruction
TOF Reconstruction and calibration well underway
CKOV reco same Tracker reconstruction
works
e+
+
+e+
+
+e+
+
+e+
+
+
Beam profile at TOF0
Linda R. Coney – 4 June 2009
Online Reconstruction Farm
Installed two farm computers in MICE control room March 09 Total of three quad-core processors G4MICE installed on both Tests run
Reconstructed tracker cosmic ray test data 114 events/second
Ran simulation, digitization, and reconstruction of Step VI Simulation: ~262 events/second Simulation + Digi: ~236 events/second Reconstruction: ~1920 events/second
Linda R. Coney – 4 June 2009
Online Reconstruction Histograms
What is needed to produce these plots? Online Reconstruction farm G4MICE installed on farm TOF reconstruction CKOV reco Tracker reco KL reco
Unpacking code for each detector TOF, CKOV, GVA, KL Trackers, EMR (08/09, late 2009)
Check that G4MICE uses unpacker in same way that Online Monitoring uses unpacker
Can produce online monitoring plots with G4MICE Testing under way to compare to standard Online Monitoring plots (6/09)
Linda R. Coney – 4 June 2009
Conclusions
We are now able to Read out and decode DATE DAQ from MICE beam data Monitor Step I raw data quality and detector performance with Online
Monitoring Reconstruct TOF, CKOV, Tracker data
We will soon Implement online reconstruction for Step I Include tracker in online monitoring for Step II
We will eventually Include necessary information for further steps Routinely have shifters monitoring detectors and MICE physics in MLCR
The MICE ScheduleThe MICE Schedule
Experiment designed to grow with each step providing important informationExperiment designed to grow with each step providing important information